Determining the different conformational states of a protein and the transition paths between them is key to fully understanding the relationship between biomolecular structure and function. This can be accomplished by sampling protein conformational space with molecular simulation methodologies. Despite advances in computing hardware and sampling techniques, simulations always yield a discretized representation of this space, with transition states undersampled proportionally to their associated energy barrier. We present a convolutional neural network that learns a continuous conformational space representation from example structures, and loss functions that ensure intermediates between examples are physically plausible. We show that this network, trained with simulations of distinct protein states, can correctly predict a biologically relevant transition path, without any example on the path provided. We also show we can transfer features learned from one protein to others, which results in superior performances, and requires a surprisingly small number of training examples.

中文翻译：

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具有卷积和潜插值的深度学习蛋白质构象空间

确定蛋白质的不同构象状态及其之间的过渡路径，是充分理解生物分子结构与功能之间关系的关键。这可以通过使用分子模拟方法对蛋白质构象空间进行采样来完成。尽管计算硬件和采样技术取得了进步，但是模拟始终会生成该空间的离散表示，并且过渡状态与相关的能垒成比例地欠采样。我们提出了一个卷积神经网络，该神经网络从示例结构中学习连续的构象空间表示形式，并确保了示例之间的中间物在物理上是合理的损失函数。我们表明，该网络经过模拟不同蛋白质状态的训练，可以正确预测生物学上相关的过渡路径，而在提供的路径上没有任何示例。我们还表明，我们可以将从一种蛋白质中学到的特征转移到另一种蛋白质上，从而获得卓越的性能，并且所需训练样本的数量惊人地少。